The Biostatistics Core supports the clinical and laboratory-based projects of this program project so that research studies are efficiently designed, conducted, monitored, and analyzed. To ensure that scientifically valid conclusions can be drawn from these experiments, this support includes statistical modeling, hypothesis formulation, clinical trial design, and data analysis. These activities in turn require development of innovative statistical methods as needed to address new statistical problems as they arise, and accompanying development of appropriate computer programs to implement these methods. All of these activities are carried out in collaboration with the laboratory and clinical investigators. Specific objectives are as follows: (1) To provide biostatistical consultation and collaboration in the planning, conduct, analysis and reporting of clinical trials in CML. These include phase I trials to determine safe and effective dose combinations, phase II trials to assess therapeutic efficacy, randomized trials to compare competing treatments and select among them, and hybrid trials that have multiple goals. Randomized trials are conducted when the primary scientific question is comparative and it is ethically and logistically appropriate to randomize. These statistical designs accommodate multiple patient outcomes in various combinations, including acute toxicity, regimen-related death, achievement of complete remission (CR), graft-versus-host disease (GVHD) in allogeneic transplantation (allotx) trials, and the times to disease remission, disease progression, onset of CML accelerated phase, blast crisis, and death. (2) To consult and collaborate with research investigators evaluating the cellular and molecular basis of response. In particular, specific therapeutic modalities for treatment of CML patients who have relapsed after initial treatment with imatinib mesylate will be evaluated. (3) To evaluate the prognostic significance of innovative treatments and their possible synergistic effects with specific biological and molecular markers, based on their presence/absence or quantitative levels over time in the patient. This includes statistical regression modeling, data analysis, and hypothesis formulation.